Several predictions for data analytics in 2016 have focused on “action” rather than just “analysis,” and one data scientist sees 2016 as the year of greater “transparency.” But that transparency will be hard for some organizations to see, as demand for limited data science talent grows.

Michael Schmidt, CEO and founder of Nutonian, says “2016 is the year that we bring transparency to all of the algorithms and statistics coming out of the data science movement. We’re at a point where we can predict incredibly well, and now it’s time to apply that in a much broader sense.”

Schmidt offered his thoughts to Information Management on where data analytics is heading this year, especially around the growing trend of data automation to ease the shortage of data scientists.

“One of the biggest challenges to overcome in the next year with a lot of these data management techniques – deep learning; finding patterns in data – is that you typically need very specific domain experts and data science experts to do that. Most companies don’t have access to that sort of talent.”

Indeed, ‘user companies’ in particular are having a difficult time finding all of the data science talent they need as market demand grows.

“Most data scientists today are not going to a Lowes or a Home Depot – they’re going to Google, or they’re going to startups, or going into finance,” Schmidt says.

This will put growing focus on two trends: data analytics automation, in which programs handle more of the data management decisions; and the tapping of more ‘business types’ to manage data initiatives, Schmidt believes.

“Even in companies that are fortune enough to have some of the data science talent there is still a significant gap – the things they find still need to be translated to a business application. The data scientist is not the person that can do that. Their expertise is with statistics and algorithms and the programming,” Schmidt explains. “You need a person that is intimately familiar with the operations of the company or the marketing of a product.”

For many organizations, that means “the process needs to be automated,” Schmidt says. “That’s where data science is going to change from a cottage industry, to where it is industrialized and is broadly applicable across the company.”

Based on his clients, Schmidt says there have been some very common themes or goals with data analytics over the past 12 months.

“I would say the most common thing is, they need to be able to do things like better forecasting,” Schmidt says. “They need more than a number. They need to justify what is going on.”

As an example, Schmidt cited a retail client that is using his firm to decide where to open new stores.

“Those are major investments -- $20 to $30 million per store,” Schmidt notes. “They need to understand strategically what is going to drive or impact the sales at different stores, at different locations. You go to your CFO and say, ‘I think the sales at the store are going to be X, because it has this and this and this.’ We automate that process – usually the type of things that a team of data scientists would traditionally do with analytics, and they would do through a very heavy manual investigation.”

While data automation tools and techniques have certainly improved, more is needed Schmidt says. “Very recently, especially in the past five years, there has been huge progress in the ability to predict,” Schmidt says. “We have very complex machine learning algorithms that can take data and pump out predictions. This is really the strength of AI -- that it puts data to work; and we’ve made tremendous progress with that.”

“The area that has been neglected, or needs the most work, is interpreting what those patterns mean; what’s behind those predictions,” Schmidt says.

“For example, deep learning is a very exciting, very hyped-up technology because it can do things that computers couldn’t do before -- like detects faces in images, or process speech, and things like that. But one of the challenges there, or where they fall short, is in interfacing with a human,” Schmidt says.

Ironically, the more data analytics techniques advance, the greater the challenge in explaining insights back to users, Schmidt says. The new insights gained add new layers of complexity.

“They add tremendous complexity,” Schmidt says. “You need that same level of accuracy; that same power; but in the simplest possible form so you can explain it back and apply it.”

“You need to be able to scale and understand the information,” Schmidt says. “In a manufacturing example, instead of being told that something is going to fail, it’s being told, ‘hey this is why it is going to fail, and here is what will happen if you change it.’